# PyPy[distribution]

PyPy[distribution]
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# (Semi)-transparent distribution of RPython programs

# Some (rough) ideas how I see distribution

The main point about it, is to behave very much like JIT - not to perform distribution on Python source code level, but instead perform distribution of RPython source, and eventually perform distribution of interpreter at the end.

This attempt gives same advantages as off-line JIT (any RPython based interpreter, etc.) and gives nice field to play with different distribution heuristics. This also makes eventually nice possibility of integrating JIT with distribution, thus allowing distribution heuristics to have more information that they might have otherwise and as well with specializing different nodes in performing different tasks.

# Flow graph level

Probably the best place to perform distribution attempt is to insert special graph distributing operations into low-level graphs (either lltype or ootype based), which will allow distribution heuristic to decide on entrypoint to block/graph/some other structure??? what variables/functions are accessed inside some part and if it's worth transferring it over wire.

# Backend level

Backends will need explicit support for distribution of any kind. Basically it should be possible for backend to remotely call block/graph/structure in any manner (it should strongly depend on backend possibilities).